We compare our main application of diverse video generation to existing single-video methods – HP-VAE-GAN [1]}, SinGAN-GIF [2]} and VGPNN [3]}.
Since none of these methods is autoregressive, the evaluations above cannot be directly applied to them. However, a qualitative observation hints that the motions in most of their generated outputs are somewhat copied from the input video. Measuring motion “novelty” in generated videos is hard, mainly because of the inherent entanglement between motion and appearance, namely, we want to consider only novel motions with similar appearance to the original motions, and penalize novel motion with unrelated appearance (e.g., random noise is novel in the sense that it is not in the input but also has unrelated appearance). SinGAN [4]} proposed a diversity measure that is easy to “fool” (see sec:foolingsingandiversity).
